Hillert Materials Modeling Colloquium series IX: The Era of Data-Driven Materials Innovation and Design
In this seminar Professor Kristin Persson talks about The Materials Project, where supercomputing is used together with state-of-the-art quantum mechanical theory to compute the properties of materials. She will highlight some of the ongoing work, including new materials development, synthesis and characterization and associated machine learning algorithmic tools and data-driven approaches.
Time: Tue 2023-01-24 15.00 - 16.00
Video link: https://kth-se.zoom.us/j/63731123840
Language: English
Participating: Professor Kristin Persson
Fueled by our abilities to compute materials properties and characteristics orders of magnitude faster than they can be measured and recent advancements in harnessing literature data, we are entering the era of the fourth paradigm of science: data-driven materials design. The Materials Project (www.materialsproject.org) uses supercomputing together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials and beyond, design novel materials and offer the data for free to the community together with online analysis and design algorithms.
The current release contains data derived from quantum mechanical calculations for over 145,000 materials and millions of properties. The resource supports a growing community of data-rich materials research, currently supporting over 300,000 registered users and millions of data records served each day through the API. The software infrastructure enables thousands of calculations per week –enabling screening and predictions – for both novel solid as well as molecular species with target properties. However, truly accelerating materials innovation also requires rapid synthesis, testing and feedback. The ability to devise data-driven methodologies to guide synthesis efforts is needed as well as rapid interrogation and recording of results – including ‘non-successful’ ones.
In this talk, I will highlight some of our ongoing work, including new materials development, synthesis and characterization and associated machine learning algorithmic tools and data-driven approaches.
Hillert Materials Modeling Colloquium Series is arranged by Hillert Modeling Laboratory
Department of Materials Science and Engineering
KTH Royal Institute of Technology
Contact Hillert Modeling Laboratory